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---
language:
- "en"
license: mit
datasets:
- glue
metrics:
- Matthews Correlation
---
# Model Card for WeightWatcher/albert-large-v2-cola
This model was finetuned on the GLUE/cola task, based on the pretrained
albert-large-v2 model. Hyperparameters were (largely) taken from the following
publication, with some minor exceptions.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://arxiv.org/abs/1909.11942
## Model Details
### Model Description
- **Developed by:** https://huggingface.co/cdhinrichs
- **Model type:** Text Sequence Classification
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** https://huggingface.co/albert-large-v2
## Uses
Text classification, research and development.
### Out-of-Scope Use
Not intended for production use.
See https://huggingface.co/albert-large-v2
## Bias, Risks, and Limitations
See https://huggingface.co/albert-large-v2
### Recommendations
See https://huggingface.co/albert-large-v2
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AlbertForSequenceClassification
model = AlbertForSequenceClassification.from_pretrained("WeightWatcher/albert-large-v2-cola")
```
## Training Details
### Training Data
See https://huggingface.co/datasets/glue#cola
CoLA is a classification task, and a part of the GLUE benchmark.
### Training Procedure
Adam optimization was used on the pretrained ALBERT model at
https://huggingface.co/albert-large-v2.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://arxiv.org/abs/1909.11942
#### Training Hyperparameters
Training hyperparameters, (Learning Rate, Batch Size, ALBERT dropout rate,
Classifier Dropout Rate, Warmup Steps, Training Steps,) were taken from Table
A.4 in,
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://arxiv.org/abs/1909.11942
Max sequence length (MSL) was set to 128, differing from the above.
## Evaluation
Matthews Correlation is used to evaluate model performance.
### Testing Data, Factors & Metrics
#### Testing Data
See https://huggingface.co/datasets/glue#cola
#### Metrics
Matthews Correlation
### Results
Training Matthews Correlation: 0.9786230864021822
Evaluation Matthews Correlation: 0.5723853959351589
## Environmental Impact
The model was finetuned on a single user workstation with a single GPU. CO2
impact is expected to be minimal.
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